User profile-based wireless device system level management

ABSTRACT

Techniques for user profile-based system level management (SLM) and creation of system level agreements of a wireless device are generally disclosed. In some examples, a predictor may be provided to predict a future task to be performed by a wireless device, including resource requirements, based at least in part on a profile of a user and at least one of a profile of a communication partner the user, an operational recommendation, a performance model or a current state. An optimizer/analyzer may be provided to generate a plurality of instructions to configure the wireless device, based at least in part on the predicted future task and resource requirement, and a quality of service requirement of the wireless device, in anticipation of having to perform the predicted task. In various examples, the predictor and the optimizer/analyzer may form a local or a remotely disposed system level manager.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application under 35 U.S.C. § 120 ofU.S. patent application No. 15/350,721, filed on Nov. 14, 2016, which isa continuation application under 35 U.S.C. § 120 of U.S. patentapplication Ser. No. 14/192,073, filed on Feb. 27, 2014, now U.S. Pat.No. 9,537,709, which in turn is a continuation application under 35U.S.C. § 120 of U.S. patent application Ser. No. 12/433,700, filed onApr. 30, 2009, now U.S. Pat. No. 8,667,109. The disclosures of U.S.patent application Ser. No. 15/350,721, U.S. patent application Ser. No.14/192,073, and U.S. patent application Ser. No. 12/433,700 are herebyincorporated by reference in their entireties.

BACKGROUND

Wireless communication networks are becoming increasingly popular thesedays. A typical wireless network may include plurality of wirelessdevices. Many wireless devices are increasingly configured with avariety of sensors such as camera, camcorder, GPS, compass, andthermometers. Traditionally, wireless devices employ operating systemsthat are not customized to the requirements and preferences of theirusers, environment and specific characteristics of the device, availableresources, likely applications and workload, and so forth. Currentapproach to operating and management wireless devices may no longer beviable as the functions and complexities of the wireless devicescontinue to increase.

BRIEF DESCRIPTION OF THE DRAWINGS

Subject matter is particularly pointed out and distinctly claimed in theconcluding portion of the specification. The foregoing and otherfeatures of the present disclosure will become more fully apparent fromthe following description and appended claims, taken in conjunction withthe accompanying drawings. Understanding that these drawings depict onlyseveral embodiments in accordance with the disclosure and are,therefore, not to be considered limiting of its scope, the disclosurewill be described with additional specificity and detail through use ofthe accompanying drawings:

FIG. 1 schematically illustrates an example wireless communicationsystem, in accordance with various embodiments of the presentdisclosure;

FIG. 2 schematically illustrates user profile-based system levelmanagement (SLM), in accordance with various embodiments of the presentdisclosure;

FIG. 3 schematically illustrates a user profile, and its use as acommunication partner profile in further detail, in accordance withvarious embodiments of the present disclosure;

FIG. 4 schematically illustrates an example life-time optimization usinguser profile-based SLM, in accordance with various embodiments of thepresent disclosure;

FIG. 5 schematically illustrates local user profile-based SLM, inaccordance with various embodiments of the present disclosure;

FIG. 6 schematically illustrates an alternative implementation of userprofile-based SLM, in accordance with various embodiments of the presentdisclosure;

FIG. 7 schematically illustrates meta profiling to further improve theeffectiveness of user profile-based SLM, in accordance with variousembodiments of the present disclosure;

FIG. 8 schematically illustrates an example prediction and optimizationusing multiple maximum likelihood (ML) prediction, in accordance withvarious embodiments of the present disclosure;

FIG. 9 illustrates an example computer suitable for practicing aspectsof user profile-based SLM, in accordance with various embodiments of thepresent disclosure; and

FIG. 10 illustrates a block diagram of an example article of manufacturehaving a computer program product for user profile-based SLM, inaccordance with various embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE DISCLOSURE

The following description sets forth various examples along withspecific details to provide a thorough understanding of claimed subjectmatter. It will be understood by those skilled in the art, however, thatclaimed subject matter may be practiced without some or more of thespecific details disclosed herein. Further, in some circumstances,well-known methods, procedures, systems, components and/or circuits havenot been described in detail in order to avoid unnecessarily obscuringclaimed subject matter. In the following detailed description, referenceis made to the accompanying drawings, which form a part hereof. In thedrawings, similar symbols typically identify similar components, unlesscontext dictates otherwise. The illustrative embodiments described inthe detailed description, drawings, and claims are not meant to belimiting. Other embodiments may be utilized, and other changes may bemade, without departing from the spirit or scope of the subject matterpresented here. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe Figures, may be arranged, substituted, combined, and designed in awide variety of different configurations, all of which are explicitlycontemplated and make part of this disclosure.

In the following description, algorithms and/or symbolic representationsof operations on data bits and/or binary digital signals stored within acomputing system, such as within a computer and/or computing systemmemory may be presented. An algorithm is generally considered to be aself-consistent sequence of operations and/or similar processing leadingto a desired result where the operations may involve physicalmanipulations of physical quantities that may take the form ofelectrical, magnetic and/or electromagnetic signals capable of beingstored, transferred, combined, compared and/or otherwise manipulated. Invarious contexts such signals may be referred to as bits, data, values,elements, symbols, characters, terms, numbers, numerals, etc. Thoseskilled in the art will recognize, however, that such terms may be usedto connote physical quantities. Hence, when terms such as “storing”,“processing”, “retrieving”, “calculating”, “determining” etc. are usedin this description they may refer to the actions of a computingplatform, such as a computer or a similar electronic computing devicesuch as a cellular telephone, that manipulates and/or transforms datarepresented as physical quantities including electronic and/or magneticquantities within the computing platform's processors, memories,registers, etc

This disclosure is drawn, inter alia, to methods, apparatus, and systemsrelated to user profile-based system level management of a wirelessdevice. The wireless device may be a next generation wireless device,which may be highly complex, providing ultra wide bandwidth, butrequiring high power budget wireless radios such as MIMO, cognitive,and/or ultra wide bandwidth transceivers. Further, the wireless devicemay communicate with human body embedded devices such pacemakers,portable kidneys and so forth.

Embodiments of the present disclosure combine coordinated developmentand deployment of models, prediction tools, optimization techniques, anddecision procedures that permit relatively efficient use of permanent(e.g. storage), perishable (computing cycles), and/or renewable (e.g.energy) resources so that user specified objective function under agiven set of constraints for a wireless device is optimized. In variousembodiments, the present disclosure includes long and short termsmodeling of the user, his/her communication partners, software andhardware of the devices, operational conditions and environment, energysupply, and other relevant predictors. In various embodiments,predictions from the data of same modality and/or conditional multimodalmodels may be utilized. In various embodiments, the models may becreated in a manner that they facilitate consequent optimization.

In various embodiments, the present disclosure may be practiced withon-line or off-line customization of how and when individual tasks ofthe wireless device is invoked and executed. In various embodiments, theprofiles of the user, the available resources, the quality of links atcertain time and place, and likely traffic, and computational andsensing tasks may be used to decide where the task will be executedusing which computation resources and which supply voltage.

In various embodiments, the present disclosure may be realized throughsystem-level management (SLM), local or remote, operating on top of theexisting operating system of a wireless device. In alternateembodiments, the present disclosure may be realized through employmentof parameterized operating systems for a wireless device. Models andpredictions for wireless device users behavior and preference,applications (e.g. communication traffic and sensing tasks), theavailable resources (e.g. energy) and operational environment (e.g.quality of wireless links) may be heuristic, parametric statistical ordata-driven. The prediction techniques may be machine learning or MonteCarlo simulations. And, a user interface may be provided for preferencesoverwriting.

The terms “predictor,” “optimizer,” “analyzer,” “system level manager,”“organizer,” “filter,” and “profiler” refer to respective hardwareand/or software implementing entities, and does not include a humanbeing. The operations performed by the “predictor” et al are operationsperformed by the respective hardware and/or software implementations,e.g. operations that transform data representative of real things fromone state to another state, and these operations do not include mentaloperations performed by a human being.

FIG. 1 schematically illustrates an example wireless communicationsystem, in accordance with various embodiments of the presentdisclosure. Wireless communication system 100 may include one or morewireless communication networks, generally shown as 110, 120 and 130. Inparticular, the wireless communication system 100 may include a wirelesslocal area network (WLAN) 110, a wireless metropolitan area network(WMAN) 120 and a wireless wide area network (WWAN) 130. Although FIG. 1depicts three wireless communication networks, the wirelesscommunication system 100 may include additional or fewer wirelesscommunication networks. For example, the wireless communication system100 may include more or less WLANs, WMANs and/or WWANs. The methods andapparatus described herein are not limited in this regard.

The wireless communication system 100 may also include one or moremobile stations, also referred to as subscriber stations, generallyshown as 140, 142 and 144. For example, the subscriber stations 140, 142and 144 may include wireless electronic devices such as a desktopcomputer, a laptop computer, a handheld computer, a tablet computer(e.g., personal digital assistant (PDA)), a cellular telephone, a pager,an audio and/or video player (e.g., an MP3 player or a DVD player), agaming device, a video camera, a digital camera, a navigation device(e.g., a global positioning satellite (GPS) device), a wirelessperipheral (e.g., a printer, a scanner, a headset, a keyboard, a mouse,etc.), a medical device (e.g., a heart rate monitor, a blood pressuremonitor, etc.), and/or other suitable fixed, portable, or mobileelectronic devices. Although FIG. 1 depicts three subscriber stations140, 142 and 144, the wireless communication system 100 may include moreor less subscriber stations.

Typically, each of the subscriber stations 140, 142 and 144 may includea number of hardware and software resources, such as processor cycles,memory space, storage, networking bandwidth, applications and so forth,and an operating system configured to manage the resources, includingallocating the resources for various tasks performed on the wirelessdevice. For various resources, the operating system may manage their useand allocation via various policies and/or parameters, e.g. processorcycle time, memory space sizes, cache sizes, networking protocols and soforth.

The subscriber stations 140, 142 and 144 may use a variety of accessschemes such as, for example, orthogonal frequency-division multipleaccess (OFDMA), spread spectrum schemes (e.g., direct sequence codedivision multiple access (DS-CDMA) and/or frequency hopping codedivision multiple access (FH-CDMA)), time-division multiple access(TDMA), frequency-division multiplexing access (FDMA) and/or othersuitable access schemes to communicate via wireless links.

In one example, the subscriber stations 140, 142 and 144 may use adirect sequence spread spectrum (DSSS) scheme and/or frequency hoppingspread spectrum (FHSS) scheme to implement the WLAN 110 (e.g.,modulations in accordance with any one of a number of industry standardsdeveloped by various standard and/or industry organizations, such as,the Institute of Electrical and Electronic Engineers (IEEE)). Forexample, the laptop computer 140 may communicate with devices associatedwith the WLAN 110 such as the handheld computer 142 and/or the cellularphone 144 via wireless links. The laptop computer 140 may alsocommunicate with an access point (AP) 150 via a wireless link.Generally, WLANs and WMANs include multiple APs 150. The AP 150 may beoperatively coupled to a router 152 as described in further detailbelow. Alternatively, the AP 150 and the router 152 may be integratedinto a single device (e.g., a wireless router).

The subscriber stations 140, 142 and 144 may use OFDMA to transmit largeamounts of digital data by splitting a radio frequency signal intomultiple small sub-signals, which in turn, may be transmittedsimultaneously at different frequencies. In particular, the subscriberstations 140, 142 and 144 may use OFDMA to implement the WMAN 120. Forexample, the subscriber stations 140, 142 and 144 may operate inaccordance with the 802.16 family of standards developed by IEEE toprovide for fixed, portable, and/or mobile broadband wireless access(BWA) networks (e.g., the IEEE std. 802.16, published by IEEE 2004) tocommunicate with base stations, generally shown as 160, 162, and 164,via wireless link(s).

Although some of the above examples are described with respect tostandards developed by IEEE, the methods and apparatus disclosed hereinare readily applicable to many specifications and/or standards developedby other special interest groups and/or standard developmentorganizations (e.g., Wireless Fidelity (Wi-Fi) Alliance, WorldwideInteroperability for Microwave Access (WiMAX) Forum, Infrared DataAssociation (IrDA), Third Generation Partnership Project (3GPP, 3GPP2),etc.). For example, long term evolution (LTE, a 3GPP standard), airinterface evolution (a 3GPP2 standard) are suitable standards forapplicability with regard to various embodiments of the presentdisclosure in addition to IEEE 802.16 and WiMAX standards. The methodsand apparatus described herein are not limited in this regard.Additionally, the subscriber stations 140, 142 and 144 may operate inaccordance with other wireless communication protocols to support theWWAN 130. In particular, these wireless communication protocols may bebased on analog, digital, and/or dual-mode communication systemtechnologies such as, for example, Global System for MobileCommunications (GSM) technology, Wideband Code Division Multiple Access(WCDMA) technology, General Packet Radio Services (GPRS) technology,Enhanced Data GSM Environment (EDGE) technology, Universal MobileTelecommunications System (UMTS) technology, standards based on thesetechnologies, variations and evolutions of these standards, and/or othersuitable wireless communication standards.

The WLAN 110, WMAN 120 and WWAN 130 may be operatively coupled to acommon public or private network 170 such as the Internet, a telephonenetwork (e.g., public switched telephone network (PSTN)), a local areanetwork (LAN), a cable network, and/or another wireless network viaconnection to an Ethernet, a digital subscriber line (DSL), a telephoneline, a coaxial cable, and/or any wireless connection, etc. In oneexample, the WLAN 110 may be operatively coupled to the common public orprivate network 170 via an AP 150 and/or the router 152. In anotherexample, the WMAN 120 may be operatively coupled to the common public orprivate network 170 via the base station(s) 160, 162, and/or 164. Inanother example, the WWAN 130 may be operatively coupled to the commonpublic or private network 170 via base station(s) 180, 182 and/or 184.

The wireless communication system 100 may include other WLAN, WMAN,and/or WWAN devices (not shown) such as network interface devices andperipherals (e.g., network interface cards (NICs)), access points (APs),redistribution points, end points, gateways, bridges, hubs, etc. toimplement a cellular telephone system, a satellite system, a personalcommunication system (PCS), a two-way radio system, a one-way pagersystem, a two-way pager system, a personal computer (PC) system, apersonal data assistant (PDA) system, a personal computing accessory(PCA) system, and/or any other suitable communication system. Althoughcertain examples have been described above, the scope of coverage ofthis disclosure is not limited thereto.

The efficient operation of wireless electronic devices within a systemsuch as system 100 may become increasingly difficult due to complexinteraction of hardware, software, applications, operational andenvironmental conditions. Almost all of these entities have numerousoptions that greatly impact the effective use of a wireless device. Forexample, high temperature may double or triple energy consumption, andhigh energy consumption may rapidly additionally increase thetemperature of a wireless device. However, the energy consumption may beimpacted by many other issues, including the allocated time for thetask, cache line policy, used scheduled algorithm, the quality ofcommunication links, and/or used bandwidth.

Therefore, in accordance with various embodiments of the presentdisclosure, a system level manager (SLM) 190 may be provided toindividual portable electronic wireless devices, such as may beillustrated by subscriber stations 140, 142, and 144 (hereinafter,simply wireless devices). In various embodiments, individual SLMs 190may comprise a predictor and an optimizer/analyzer operatively coupledto each other. In various embodiments, the predictor may be configuredto predict a future task to be performed by a wireless device, includingresource requirement for performing the future task by the wirelessdevice, based at least in part on a profile of a user of the wirelessdevice and at least one of a profile of a communication partner theuser, an operational recommendation for the wireless device, aperformance model of the wireless device and/or a current state of thewireless device. The optimizer/analyzer may be configured to generate aplurality of instructions to configure the wireless device, based atleast in part on the predicted future task and resource requirement forperforming the future task and a quality of service requirement of thewireless device, in anticipation of the wireless device having toperform the predicted task.

In various embodiments, on determination of the instructions, individualSLMs 190 may convey to the operating system the management action to betaken. The process may be repeated periodically, or performedcontinuously by SLM 190, to allow the performance of the wireless deviceto be managed periodically or continuously to achieve the quality ofservice requirements, as required.

In accordance with various embodiments, an SLM 190 may be generic forone type of wireless device (e.g. a particular type of cell phone or aparticular type and/or configuration of a laptop computer), or maytarget a specific device that may be unique due to softwarecustomization or due to hardware factors such as manufacturingvariability and aging of device components. It may target both devicesthat allocate their resources using standard or real-time operatingsystems.

In alternate embodiments, some or all aspects of SLM 190 may beintegrated with the operating system of that wireless device. In stillother embodiments, SLM 190 may be disposed on a remote computing device(not shown) instead. The remotely disposed SLM 190 may remotely manageone or more wireless devices in the network.

In various embodiments, the operating system of wireless devices 140,142, and 144 may be configured to provide measurements for various task,hardware and/or software related metrics, e.g. processor cycle time,cache hit, packet error rate and so forth. In various embodiments,wireless devices 140, 142, and 144 may be configured with sensors tomeasure and to provide measurements for various user and/or devicerelated metrics, e.g. temperature sensors, user bio-metric sensors andso forth. In various embodiments, communication system 100 may beconfigured with sensors to measure and/or repositories to store, and toprovide measurements for various environment related metrics, e.g.ambient temperature and/or humidity, network traffic bandwidth and/orerror rates. In various embodiments, these sensors or data collectionunits may be configured to take real time measurements, and/or takemeasurements continuously or periodically. SLM 190 may utilize thesedevice and/or system capabilities in obtaining the measurements.

FIG. 2 schematically illustrates user profile-based system levelmanagement (SLM), in accordance with various embodiments of the presentdisclosure. Optimization under uncertainty may be much more difficultand less effective than one where the future actions may be at leastpartially known. For example, majority of on-line algorithms formajority of combinatorial and continuous optimization problems may besignificantly inferior to the algorithms that assume complete knowledgeabout the pertinent problem instance. Some operating systems exactlyoperate in one such scenario. For example, such operating systems maymodify the operating voltage without knowing either the status of thebattery or pending tasks in near future. The present disclosureaddresses these disadvantages through a user profile-based system levelmanagement (SLM) approach that may profile a user of a wireless device,the user's communicating partners, and environment (e.g. reception rateof links) in such a way that they facilitate prediction and managementof the pending resource requirements and supply of resources, such asenergy.

As illustrated, for one or more embodiments, predictor 212 may beprovided and configured to predict 232 a future task 214 to be performedby a wireless device, including resource requirement for performing thefuture task by the wireless device. For one or more embodiments,predictor 212 may make a prediction based at least in part on a profile202 of a user of the wireless device, and at least one of a profile 204of a communication partner of the user, an operational recommendation206 for the wireless device, a performance model 208 of the wirelessdevice and/or a current status 210 of the wireless device. Note thatsome of the communication partners may be service providers, e.g. emailservice providers, telephony service providers, instant messagingservice providers, movie or music distributors. Further,optimizer/analyzer 216 may be provided and configured to generate 234 aplurality of instructions 218 to configure the wireless device, based atleast in part on the predicted future task 214 and resource requirementfor performing the future task and a quality of service requirement ofthe wireless device, in anticipation of the wireless device having toperform the predicted task.

Profiles 202 and 204 may contain information about the habits andpreferences of the user and his/her communication partners, in terms ofthe invoked tasks and their properties. For example, profiles 202 and204 may have information about the send and received emails, voicecalls, instant messages, invoked games, accessed web sites, downloadedmovies/music, as well as the accepted and favored quality of service interms of latency, throughput, resolution, security, and privacy.Profiles 202 and 204 may also contain the information about batteryrecharging patterns. The information may be abstracted to statisticalmodels and typical patterns that may be either frequency-based orformulated in terms conditional probabilities. Thus, profiles 202 and204 may contain statistical, probabilistic, and heuristic informationabout the probability that a particular task will be initiated orterminated at a particular moment or a particular scenario. For example,profiles 202 and 204 may contain the information about the expectednumber of send and received emails, voice calls, and/or instant messagesin the next hour, and/or the expected number of files, movies and/ormusic downloads in the next 24 hours (given the current and/or recentcontext of activities and/or conditions). Furthermore, profiles 202 and204 may contain information about the properties of emails, calls,instant messages, games, web sites/pages, files, movies and/or songs,such as their size/duration, their rate of arrival/invocation, and soforth.

Profiles 202 and 204 may be established by observing and extrapolatingthe previous behavior of the user. The profiles may be unconditional orconditional on time, location, mobility pattern, or local or globalevents. For example, the user may be interested to receive an emailalert any time when a subset of stocks or managed funds changed theirvalue by more than a specified amount.

Recommendations 206 may comprise one or more operational recommendationsfor the wireless devices. Recommendations 206 may be mainly related tothe resource required to execute a particular task and how they may beoptimized for various objectives. Recommendations 206 may be provided byother users who have already conducted the same or similar tasksexecutions. For example, recommendations 206 may comprise one or moredisplay resolution recommendations (depending on the items or media typeof the items to be displayed) for the wireless device, one or moremodulation recommendations (depending on the items, media type of theitems to be transmitted and/or network condition) for the wirelessdevice, one or more error correction code recommendations (depending onintegrity needs) for the wireless device, and one or more packet sizerecommendations for communication for the wireless device (depending onthe items, media type of the items to be transmitted and/or networkcondition).

Performance models (or simply model) 208 may comprise performanceinformation about the wireless device, its software, operationalconditions and the environment. For example, models 208 may containinformation about the quality of the links at a particular location interms of the signal-to-noise ratio, reception rate, interference, andcurrent and expected traffic congestion. Models 208 may also comprisetemperature and/or energy consumption of the wireless device undercertain operating conditions. In various embodiments, models 208 may beconstructed, based at least in part on measurements obtained for thedevice under the various operating and/or environmental conditions.

Current tasks and system status 210 may comprise information about thecurrent tasks, their resource consumption, in terms of e.g. permanent(e.g. storage), perishable (computing cycles), or renewable (e.g.energy) resources. Current tasks and system status 210 may comprisecurrent latency of each task, the available energy, the temperature of asubset of the components, ambient light and so forth.

The resource requirement of the predicted future task 214 may comprisee.g. its processor cycle requirements, its caching resourcerequirements, its transmit/receive requirements, and/or other executionrequirements.

Continuing to refer to FIG. 2, in various embodiments, predictor 212 maybe configured to provide short term higher accuracy as well as longerterms low accuracy predictions 214. Predictor 212 may employ simulationand/or Monte Carlo method. Predictions 214 may be in turn may be used byoptimizer/analyzer 216 to optimize and manage resource allocation andprotocol and policy selections. In various embodiments, as describedearlier, optimizer/analyzer 216 based at least in part on thepredictions and their resource requirements, may generate a set ofinstructions for the operating system of the wireless device to guidethe operating system in allocating and managing resource.

In various embodiments, optimizer/analyzer 216 may be configured tooptimize a complex objective subject to a complex set of constraints infast and effective way (to generate a set of instructions for apredicted task, or generate a consolidated/optimized set of instructionsfor a number of predicted tasks). In various embodiments, theoptimization may alter the operational parameters, algorithms and/orprotocols of the wireless device, in order to improve quality of serviceobjective of the wireless device, such as life time maximization orimprovement, or the optimization or improvement of the quality ofservice in terms of operational parameters such as used resolution andlatency. In various embodiments, optimizer/analyzer 216 may assume thepredictions 214 for the future tasks and their resource requirements tobe correct. Therefore, in such a way, for these embodiments, theuncertainty may be reduced or eliminated. In various embodiments, inorder to maximize or improve the effectiveness and the accuracy,optimizer/analyzer 216 may be configured to consider during optimizationseveral likely future scenarios, and select the solution that performsbest over many or all of the considered scenarios.

In various embodiments, optimizer/analyzer 216 may be configured to useengineering change paradigm to incrementally keep improving theavailable solution and adapting to changed requirements and availableresources. In various embodiments, optimizer/analyzer 216 may beconfigured to use several variants of maximum likelihood, whereoptimizer/analyzer 216 may be configured to find the most likelyparameter set-up that may be most likely to optimize the objectivefunction for the most likely scenarios. In various embodiments, formultiple predicted tasks, optimizer/analyzer 216 may first respectivelygenerate configuration instructions for the tasks, and then generate anoptimize set of configuration instructions based on the respectivelygenerated set of instructions for the tasks.

In various embodiments, the profile building and/or optimization may beperformed either locally (on the wireless device) or at the remotecomputer. In various embodiments, the decision with respect to whereeach operation may be performed, may itself be handled using existinguser profile for SLM to allocate the tasks in such a way that the energyconsumption, latency, the accuracy, or other user specified objectivefunction may be optimized. The actual interaction of the userprofile-based SLM may be effectuated in several ways. For example, theSLM may exchange the information with modified operating system oroperating system that is designed in such a way that they may interactwith a SLM.

In various embodiments, user interface 220 may be provided to inform theuser about some or all SLM aspects. In various embodiments, userinterface 220 may be further configured to allow the user to overwriteeither directly or using more generic specification any subset of thedecision or the process.

FIG. 3 schematically illustrates a user profile and its use as acommunication partner profile in further details, in accordance withvarious embodiments of the present disclosure. As illustrated, userprofile 300, in addition to profile information 302 related to theuser's behavior with respect to email, voice calls, instant messages,games, files, web sites, movies and/or songs, user profile 300 may alsocomprise general communication attributes 304 with other parties ingeneral, user preferences 306, specific communication attributes 308with selected parties (e.g., dominant parties), and privacy and securityattributes 310.

Communication attributes 304 and 308 may comprise information aboutreception rate, latency, throughput, backlog etc. for othercommunication parties in general or special parties in particular,respectively. User preferences 306 may comprise information about theuser's communication preferences, e.g. display resolution (for email,instant messaging and so forth), modulation, packet size and/or errorcorrection code. Privacy and security attributes 310 may compriseinformation about e.g. the user's location, mobility mode andcommunication activity.

For one or more embodiments, information organizer 312 may be providedand configured to organize the various information into both recent andlong term information to permit the more relevant information be shared(subject to the user's privacy and security preferences) forcommunication and cooperation. For one or more embodiments, informationfilter 314 may be provided and configured to filter the organizedinformation for release to a particular communication partner.

In various embodiment, information communicator and negotiator 316 mayalso be provided and configured, to decide when and how to communicatethe releasable information to a communication partner or a group ofcommunication partners. In various embodiments, information communicatorand negotiator 316 may also be configured to negotiate with anotherparty for the what, when and how to communicate the releasableinformation.

FIG. 4 schematically illustrates an example life-time optimization usinguser profile-based SLM, in accordance with various embodiments of thepresent disclosure. The example illustrates how energy usage may beoptimized for a wireless device, using user profile-based SLM. Asillustrated, the user 402 and his/her communicating partners 404 provideinformation about the predicted future tasks 406. As described earlier,the predicted future tasks may include information about the requiredresources associated with the predicted future task. For the example,this information may be used to create a set of likely expect workloadsfor the wireless device.

For the example, the optimizer/analyzer consequently may conductoptimization at one or more of two levels: (i) at the individual tasklevel; and (ii) at the interaction level. As illustrated and earlierdescribed, the optimizer/analyzer first may generate instructions 408for the wireless device to configure itself for each of the predictedfuture task. As illustrated and described earlier, the instructions 408may comprise properties such as which processor to use, which softwareto employ, and so forth. Thereafter, the optimizer/analyzer may furtheranalyze the various sets of instructions 408, and may search forimproved or the best overall parameters and policies 410, for rapid andaccurate evaluation; and stopping criteria. For one or more embodiments,on determination, the optimizer/analyzer may communicates the decision410 to the operating system and communication hardware and system aboutthe selected most or more beneficial parameter setting and selectedalgorithms, protocols, and policies, including e.g. the device voltageVdd, buffer allocation, player selection, and so forth, whereapplicable.

As with the earlier described embodiments, user interface 420 may beprovided to inform the user about some or all SLM aspects. In variousembodiments, user interface 420 may be further configured to allow theuser to overwrite either directly or using more generic specificationany subset of the decision or the process.

FIG. 5 schematically illustrates local user profile-based SLM, inaccordance with various embodiments of the present disclosure. For theseembodiments, the pertinent wireless device 504 does some or allprofiling and modeling associated with user profile 502, as well aspredictions, and optimization itself. Some benefits may includeself-sufficiency and reduction of communication cost. Note that thecommunication with other communication partners 506 and 508 may still beutilized at least for coordination about the communication set-up. Forexample, information about the communicating times, used modulation,synchronization code, used frequencies, and the error correction codemay be coordinated between the communication partners 506 and 508. Themain disadvantage may be that often more simplistic profiling, modeling,and optimization may be conducted in order to avoid the overloading ofthe local resources of the wireless device 504.

FIG. 6 schematically illustrates an alternative implementation of userprofile-based SLM, in accordance with various embodiments of the presentdisclosure. For these embodiments, at least part of the profiling,modeling, predictions, and optimization for communication withcommunication partners 606 and 608 may be done at a remote computingdevice 610 (in lieu of the target wireless device 604), using userprofile 602. One advantage for these embodiments may be that now each ofprofiling, modeling, predictions, and optimization may be done in moreelaborated way and that coordination may be better achieved. However,some of the privacy may be reduced, the communication cost may benon-trivial and that the latency of decision may be increased.

In various embodiments, both local and remote user profile-based SLM maybe used in such a way that the advantages may be combined and thelimitations of each individual scheme may be reduced.

FIG. 7 schematically illustrates meta profiling to further improve theeffectiveness of user profile-based SLM, in accordance with variousembodiments of the present disclosure. As illustrated, for theseembodiments, the instructions sets 708 generated by optimizer/analyzer706 for the predicted future tasks 704, include meta data describing theinformation used in generating the instructions. For these embodiments,a meta profiler 710 may be further provided and configured to analyzethe meta data included with the instruction sets 708 to determine whichinformation was used by the various optimizer/analyzers 706 ingenerating the various instructions for various predicted future tasks,and their relevance. As earlier described, the variousoptimizer/analyzers 706 generate the instructions based at least in parton the predicted future tasks 704, which may be predicted based at leastin part on the user and their communication partners profiles 702.

For these embodiments, meta profiler 710 analyzes these information andtheir relevance 708 and generates conclusions 712 for the variousoptimizers/analyzers 706 with respect what operational parameters shouldbe optimized, what device performance characteristics should be modeled,what kind of user behavior attributes should be profiled, and so forth.Conclusions 712 may also include information identifying thoseparticular operations that may be actually most practically importantfor a specific wireless device and/or a specific user.

As illustrated, the conclusions 712 may be feedback to theoptimizers/analyzers 706 for optimizers/analyzers 706 to improve theiroptimization and analysis. This process of analyzing what informationused in generating instructions, their relevance, and feeding back tothe optimizer/analyzer 706 may be repeated periodically or continuously.Accordingly, the effectiveness of the user profile-based SLM approachmay be further improved.

FIG. 8 schematically illustrates an example prediction and optimizationusing multiple maximum likelihood (ML) predictions, in accordance withvarious embodiments of the present disclosure. In various embodiments,the optimizer/analyzer performs the ML SLM optimization in the followingway. The predictor first may use the information from the profiles 802and the system current status 804 to generate a number of highly likelysets 806 of tasks, e.g. a set having tasks {T1, T2, T3}, another sethaving tasks {T2, T3, T4} and so forth. For each set 806 of predictedtasks, the optimizer/analyzer may find the best selection of parameters808-814. These selections of parameters 808-814 may provide informationabout likelihood that each of the decision may be most beneficial and towhat extent. Next, the optimizer/analyzer may combine the informationabout likelihood using ML prediction to generate the combinedoptimization solution 816 that may be most likely optimize the pertinentquality of service objective function under the specified constraints.For various embodiments, if some task is actually not executed, theinstructions associated with such tasks may be skipped in thedetermination of the combined optimization solution 816. On the otherhand, if an unexpected task is invoked initially, the task may beperformed using the set-up that is most beneficial without consideringother tasks and the SLM process is invoked. In alternate embodiments,variations of these basic schemes may be executed.

FIG. 9 illustrates an example computer suitable for practicing aspectsof user profile-based SLM, in accordance with various embodiments of thepresent disclosure. FIG. 9 includes a computer 900, including aprocessor 902, memory 904 and one or more drives 906. The drives 906 andtheir associated computer storage media, provide storage of computerreadable instructions, data structures, program modules and other datafor the computer 900. The drives 906 may include an operating system908, application programs 910 and program modules 912. For one or moreembodiments drives 906 further include programming instructions 907implementing various earlier described aspects of user profile-basedSLM.

The computer 900 may further include user input devices 916 throughwhich a user may enter commands and data. Input devices may include anelectronic digitizer, a microphone, a keyboard and pointing device,commonly referred to as a mouse, trackball or touch pad. Other inputdevices may include a joystick, game pad, satellite dish, scanner, orthe like.

These and other input devices may be coupled to processor 902 through auser input interface that is coupled to a system bus 918, but may becoupled by other interface and bus structures, such as a parallel port,game port or a universal serial bus (USB). Computers such as thecomputer 900 may also include other peripheral output devices such asspeakers, which may be coupled through an output peripheral interface920 or the like.

The computer 900 may operate in a networked environment using logicalconnections to one or more computers, such as a remote computer coupledto network interface 922. The remote computer may be a personalcomputer, a server, a router, a network PC, a peer device or othercommon network node, and may include many or all of the elementsdescribed above relative to the computer 900. Networking environmentsare commonplace in offices, enterprise-wide area networks (WAN), localarea networks (LAN), intranets and the Internet. For example, in thesubject matter of the present application, the computer 900 may comprisethe source machine from which data is being migrated, and the remotecomputer may comprise the destination machine or vice versa. Note,however, that source and destination machines need not be coupled by anetwork 924 or any other means, but instead, data may be migrated viaany media capable of being written by the source platform and read bythe destination platform or platforms. When used in a LAN or WLANnetworking environment, the computer 900 is coupled to the LAN through anetwork interface 922 or an adapter. When used in a WAN networkingenvironment, the computer 900 typically includes a modem or other meansfor establishing communications over the WAN, such as the Internet ornetwork 924. It will be appreciated that other means of establishing acommunications link between the computers may be used.

Articles of manufacture and/or systems may be employed to perform one ormore methods as disclosed herein. FIG. 10 illustrates a block diagram ofan example article of manufacture having a computer program product 1000for user profile-based system level management (SLM), in accordance withvarious embodiments of the present disclosure. The computer programproduct 1000 may comprise computer readable storage medium 1032 andplurality of programming instructions 1034 stored in the computerreadable storage medium 1032. In various ones of these embodiments,programming instructions 1034 may be adapted to program a processor ofan apparatus, such that when the programming instructions are executedby the processor, the programming instructions enable the apparatus toperform one or more of receiving a user profile, a communication partnerprofile, a recommendation, a performance model and/or a current systemstatus of a wireless device; predicting a future task including resourcerequirement for the wireless device based at least in part on the userprofile and at least one of the received communication partner profile,recommendation, performance model and/or current system status;generating one or more instructions to configure the wireless device toperform the predicted future task based at least in part on the resourcerequirement and a quality of service requirement; and/or conveying orperforming the generated instructions.

Computer readable storage medium 1032 may take a variety of formsincluding, but not limited to, non-volatile and persistent memory, suchas, but not limited to, compact disc read-only memory (CDROM) and flashmemory.

Claimed subject matter is not limited in scope to the particularimplementations described herein. For example, some implementations maybe in hardware, such as employed to operate on a device or combinationof devices, for example, whereas other implementations may be insoftware and/or firmware. Likewise, although claimed subject matter isnot limited in scope in this respect, some implementations may includeone or more articles, such as a storage medium or storage media. Thisstorage media, such as CD-ROMs, computer disks, flash memory, or thelike, for example, may have instructions stored thereon, that, whenexecuted by a system, such as a computer system, computing platform, orother system, for example, may result in execution of a processor inaccordance with claimed subject matter, such as one of theimplementations previously described, for example. As one possibility, acomputing platform may include one or more processing units orprocessors, one or more input/output devices, such as a display, akeyboard and/or a mouse, and one or more memories, such as static randomaccess memory, dynamic random access memory, flash memory, and/or a harddrive.

There is little distinction left between hardware and softwareimplementations of aspects of systems; the use of hardware or softwareis generally (but not always, in that in certain contexts the choicebetween hardware and software may become significant) a design choicerepresenting cost vs. efficiency tradeoffs. There are various vehiclesby which processes and/or systems and/or other technologies describedherein may be effected (e.g., hardware, software, and/or firmware), andthat the favored vehicle may vary with the context in which theprocesses and/or systems and/or other technologies are deployed. Forexample, if an implementer determines that speed and accuracy areparamount, the implementer may opt for a mainly hardware and/or firmwarevehicle; if flexibility is paramount, the implementer may opt for amainly software implementation; or, yet again alternatively, theimplementer may opt for some combination of hardware, software, and/orfirmware.

In some embodiments, several portions of the subject matter describedherein may be implemented via Application Specific Integrated Circuits(ASICs), Field Programmable Gate Arrays (FPGAs), digital signalprocessors (DSPs), or other integrated formats. However, those skilledin the art will recognize that some aspects of the embodiments disclosedherein, in whole or in part, may be equivalently implemented inintegrated circuits, as one or more computer programs running on one ormore computers (e.g., as one or more programs running on one or morecomputer systems), as one or more programs running on one or moreprocessors (e.g., as one or more programs running on one or moremicroprocessors), as firmware, or as virtually any combination thereof,and that designing the circuitry and/or writing the code for thesoftware and or firmware would be well within the skill of one of skillin the art in light of this disclosure. In addition, those skilled inthe art will appreciate that the mechanisms of the subject matterdescribed herein are capable of being distributed as a program productin a variety of forms, and that an illustrative embodiment of thesubject matter described herein applies regardless of the particulartype of signal bearing medium used to actually carry out thedistribution. Examples of a signal bearing medium include, but are notlimited to, the following: a recordable type medium such as a floppydisk, a hard disk drive (HDD), a compact disc (CD), a digital video disk(DVD), a digital tape, a computer memory, etc.; and a transmission typemedium such as a digital and/or an analog communication medium (e.g., afiber optic cable, a waveguide, a wired communication link, a wirelesscommunication link, etc.).

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art may translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity.

Although certain example methods, apparatus, and articles of manufacturehave been described herein, the scope of coverage of this disclosure isnot limited thereto. On the contrary, this disclosure covers allmethods, apparatus, and articles of manufacture fairly falling withinthe scope of the appended claims either literally or under the doctrineof equivalents. For example, although the above discloses examplesystems including, among other components, software or firmware executedon hardware, it should be noted that such systems are merelyillustrative and should not be considered as limiting. In particular, itis contemplated that any or all of the disclosed hardware, software,and/or firmware components could be embodied exclusively in hardware,exclusively in software, exclusively in firmware or in some combinationof hardware, software, and/or firmware.

What is claimed is:
 1. A method to manage a wireless device in awireless environment, the method comprising: predicting a task to beperformed by the wireless device and a resource requirement associatedwith the predicted task, wherein the prediction is based at least, inpart, on a profile associated with the wireless device and anoperational recommendation associated with the wireless device;generating a set of instructions to configure the wireless device,wherein the generation of the set of instructions is based at least, inpart, on the predicted task, the predicted resource requirementassociated with the predicted task, and a quality of service requirementof the wireless device to perform the predicted task; providing thegenerated set of instructions to an operating system of the wirelessdevice, wherein the generated set of instructions is usable by theoperating system of the wireless device to manage and allocateresources.
 2. The method of claim 1, further comprising: providing thegenerated set of instructions on a user interface of the wirelessdevice.
 3. The method of claim 1, further comprising: analyzingmetadata, related to the generation of the set of instructions, togenerate one or more conclusions associated with the wireless device,wherein the one or more conclusions include information regarding atleast one of: optimization of operational parameters of the wirelessdevice, modeling of performance characteristics of the wireless device,and profiling of one or more behavioral attributes of a user associatedwith the wireless device; and providing, based on the generated one ormore conclusions, feedback for the generation of the set ofinstructions.
 4. The method of claim 1, wherein the operationalrecommendation associated with the wireless device includes at least oneof: display resolution recommendations, communication modulationrecommendations, error correction code recommendations, and packet sizerecommendations.
 5. The method of claim 1, wherein the profileassociated with the wireless device comprises one or more profilesassociated respectively with one or more communication partners of thewireless device, and wherein: the one or more communication partners ofthe wireless device include network service providers, and the one ormore profiles associated respectively with the one or more communicationpartners of the wireless device include information about voicecommunication via the wireless device over one or more networks servicedby the one or more communication partners of the wireless device.
 6. Themethod of claim 1, wherein predicting the resource requirementassociated with the predicted task includes predicting at least one of:a processor cycle requirement, a caching resource requirement, and atransmit-receive resource requirement.
 7. The method of claim 1, whereinthe method is performed by the wireless device.
 8. The method of claim1, wherein the method is performed by a remote wireless device, andwherein the remote wireless device manages one or more wireless devicesin the wireless environment.
 9. The method of claim 1, wherein theprofile associated with the wireless device includes a profile of a userassociated with the wireless device, and wherein the profile of the userincludes information about preferences, of the user, related to thepredicted task.
 10. A wireless device, comprising: at least oneprocessor configured to perform or cause to be performed: predict a taskto be performed by the wireless device and a resource requirementassociated with the predicted task, wherein the prediction is based atleast, in part, on a profile associated with the wireless device and anoperational recommendation associated with the wireless device; generatea set of instructions to configure the wireless device, wherein thegeneration of the set of instructions is based at least, in part, on thepredicted task, the predicted resource requirement associated with thepredicted task, and a quality of service requirement of the wirelessdevice to perform the predicted task; and provide the generated set ofinstructions to an operating system of the wireless device, wherein thegenerated set of instructions are usable by the operating system of thewireless device to manage and allocate resources; and a user interfacecommunicatively coupled to the at least one processor, wherein the userinterface is configured to: inform a user associated with the wirelessdevice about the generated set of instructions; and allow the userassociated with the wireless device to overwrite the generated set ofinstructions.
 11. The wireless device of claim 10, wherein the at leastone processor is further configured to perform or cause to be performed:analyze metadata, related to the generation of the set of instructions,to generate one or more conclusions associated with the wireless device,wherein the one or more conclusions include information regarding atleast one of: optimization of operational parameters of the wirelessdevice, modeling of performance characteristics of the wireless device,and profiling of one or more attributes of a user associated with thewireless device; and utilize the generated one or more conclusionsassociated with the wireless device, as feedback for the generation ofthe set of instructions.
 12. The wireless device of claim 10, whereinthe operational recommendation associated with the wireless deviceincludes at least one of: display resolution recommendations,communication modulation recommendations, error correction coderecommendations, and packet size recommendations.
 13. The wirelessdevice of claim 10, wherein the profile associated with the wirelessdevice comprises one or more profiles associated respectively with oneor more communication partners of the wireless device, and wherein: theone or more communication partners of the wireless device includenetwork service providers, and the one or more profiles respectivelyassociated with the one or more communication partners of the wirelessdevice include information about voice communication via the wirelessdevice over one or more networks serviced by the one or morecommunication partners of the wireless device.
 14. The wireless deviceof claim 13, further comprising: a network interface configured toaccess, based on network conditions, at least one wireless networkserviced by one of the network service providers, wherein: the at leastone wireless network serviced by the one of the network serviceproviders includes a cellular network or a Wi-Fi network, and thenetwork conditions include at least one of: latency, throughput, andresolution associated with the wireless network.
 15. An apparatus tomanage a wireless device, the apparatus comprising: a first processorconfigured to perform or cause to be performed: predict a task to beperformed by the wireless device and a resource requirement associatedwith the predicted task, wherein the prediction is based at least, inpart, on a profile associated with the wireless device and a performancemodel associated with the wireless device; generate a set ofinstructions to configure the wireless device, wherein the generation ofthe set of instructions is based at least, in part, on the predictedtask, the predicted resource requirement associated with the predictedtask, and a quality of service requirement of the wireless device toperform the predicted task; analyze the generated set of instructions toidentify a set of parameters associated with the wireless device,wherein the identified set of parameters are to be configured for thepredicted task, and wherein the identified set of parameters areassociated with an individual task level optimization and an interactionlevel optimization of the wireless device; and communicate theidentified set of parameters to one of a communication hardware or anoperating system of the wireless device; and a second processor,communicatively coupled to the first processor, configured to perform orcause to be performed: analyze metadata, related to the generation ofthe set of instructions, to generate one or more conclusions associatedwith the wireless device, wherein the one or more conclusions includeinformation regarding at least one of: optimization of operationalparameters of the wireless device, modeling of performancecharacteristics of the wireless device, and profiling of one or moreattributes of a user associated with the wireless device; and utilizethe generated one or more conclusions associated with the wirelessdevice, as feedback for the generation of the set of instructions. 16.The apparatus of claim 15, wherein the identified set of parametersinclude information associated with one or more of: an operationalvoltage of the wireless device, a buffer allocation for the wirelessdevice, one or more policies associated with an operation of thewireless device, and one or more protocols associated with the operationof the wireless device.
 17. The apparatus of claim 15, wherein theperformance model includes one or more of: a signal-to-noise ratiomodel, a device temperature model, a communication reception rate model,and a communication interference model.
 18. The apparatus of claim 15,wherein the first processor is further configured to perform or cause tobe performed: predict another task to be performed by the wirelessdevice and a resource requirement associated with the another task,wherein the prediction of the another task and the resource requirementassociated with the another task is based at least, in part, on theprofile associated with the wireless device and the performance modelassociated with the wireless device; generate an additional set ofinstructions to configure the wireless device, wherein the generation ofthe additional set of instructions is based at least, in part, on theanother predicted task, the predicted resource requirement associatedwith the another future task, and a quality of service requirement ofthe wireless device to perform the predicted another task; and generatean optimal set of instructions for the wireless device, wherein thegeneration of the optimal set of instructions is based at least, inpart, on the generated set of instructions for the predicted task andthe generated additional set of instructions for the another predictedtask.
 19. The apparatus of claim 15, wherein the predicted resourcerequirement includes at least one of: a processor cycle requirement, acaching resource requirement, and a transmit-receive resourcerequirement.
 20. The apparatus of claim 15, wherein the profileassociated with the wireless device includes one of a user profile or acommunication partner profile, and wherein: the user profile comprisesinformation about emailing, voice calling, instant messaging, moviewatching, or song listening behavior, of the user, associated with thewireless device, and the communication partner profile comprisesinformation about emailing, voice calling, instant messaging, moviewatching, or song listening behavior, of a communication partner.